Research Interests

(Past) Research Projects

Surgical workflow recovery is a crucial step towards the development of intelligent support systems in surgical environments. The objective of the project is to create a system which is able to recognize automatically the current steps of a surgical laparoscopic procedure using a set of signals recorded from the OR. The project adresses several issues such as the simultaneous recordings of various signals within the OR, the design of methods and algorithms for processing and interpreting the information, and finally the development of a convenient user interface to display context sensitive information inside the OR. The current clinical focus is on laparoscopic cholecystectomies but the concepts developed in the project also apply to laparoscopic surgeries of other kinds.

Providing feedback to trainees is one of the most important issues to support learning. This project researches how to give quantitative and visual feedback on the performance of a student when training on simulators or phantoms. Statistical analysis and probabilistic models are used to compare the performance of students and experts. Augmented Reality and video is used to give visual feedback like a synchronized replay of the student’s and the expert’s performance.

Angiographic images visualize vascular structure in different modalities like X-Ray, CT, or MR data sets. In many medical applications, a registration and proper visualization of the data sets, especially the vasculature is useful for a better navigation. The focus of this project lies on 2D/3D registration of angiographic data where intensity-based, feature-based, and hybrid approaches are evaluated, the latter two of them requiring an accurate 2D and 3D segmentation of the data. The main clinical partner is the radiology department of the Universitätsklinikum Großhadern (Ludwig-Maximilian Universität München) , industrial partner is Siemens Medical Solutions, Forchheim.

Stroke is the third leading cause of death in Germany. It is a neurology injury, whereby the oxygen supply to parts of the brain gets cut off. About 80% of these strokes are due to ischemia, i.e. an occlusion of a blood vessel leading to an interrupted blood flow. Stenosis inside the carotid artery imaged using four different MR weightings Special setting in this project is the arteria carotis. Plaque is most likely to develop at the branching of the arteria carotis communis into the arteria carotis interna (leading to the brain) and the arteria carotis externa. This can lead to an abnormal narrowing, called a stenosis. According to the American Heart Association these plaques can be divided into different types, based on their consistency and structure. Until now the decision about a surgery was only based on the degree of the stenosis and not on the type of plaque causing it. This is a faulty approach since there is a plaque type (Type IV) which constitutes a relevant clinical danger, although it does not necessary come along with a stenosis. Unlike most other image modalities MR images do not only give information about the degree of the stenosis, but also about the consistency of the plaque. Using different weighted MR images it is possible to correctly classify plaque into the types defined by the AHA. The main goal of this project is to create a classification tool based on T1, T2, Proton Density and 'Time of flight' weighted images. To achieve this goal the arteria carotis and the plaque have to be segmented from the images. Furthermore various features of the plaque have to be extracted in order to get information needed for the classification.

In this project, we aim at discovering automatically the workflow of percutaneous vertebroplasty. The medical framework is quite different from a parallel project , where we analyze laparoscopic surgeries. Contrary to cholecystectomies where much information is provided by the surgical tools and by the endoscopic video, in vertebroplasties and kyphoplasties, we believe that the body and hand movement of the surgeon give a key insight into the surgical activity. Surgical movements like hammering of the trocar into the vertebra or the stirring of cement compounds are indicative of the current workflow phase. The objectives of this project are to acquire the workflow related signals using accelerometers, processing the raw signals and detecting recurrent patterns in order to objectively identify the low-level and high-level workflow of the procedure.

Many medical applications such as registration or tracking can be seen as the optimization of an objective function which involves a data term or similarity measure. Classical similarity measures rely for instance on image intensities, gradients or intensity statistics. In the case of noise or background clutter which is very frequent in the case of medical imaging, they might lead to registration/tracking errors. In this project, we investigate different approches and applications of learning a similarity measure directly from the data, leading to a more robust data term which is adapted to the image characteristics.

This project targets the worklow analysis of an interventional room equipped with 16 cameras fixed on the ceiling. It uses real-time 3D reconstruction data and information from other available sensors to recognize objects, persons and actions. This provides complementary information to specific procedure analysis for the development of intelligent and context-aware support systems in surgical environments.